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<title>The FORTE Knowledge Refinement System </title>

<h1>FORTE</h1> <br> 


FORTE (First Order Revision of Theories from Examples) is a machine
learning system for modifiying a first-order Horn-clause domain theory
to fit a set of training examples.  FORTE uses a hill-climbing
approach to revise theories.  It identifies possible errors in an
input theory and calls on a library of operators to develop possible
revisions.  These operators are constructed from methods such as
propositional theory refinement, first-order induction, and inversion
resolution. <p>

The FORTE system is available via <!WA0><!WA0><!WA0><a
href="ftp://ftp.cs.utexas.edu/pub/mooney/forte"> anonymous ftp</a>.
This system contains the following items: <p>
<ol>
<li>Quintus Prolog source code for Forte.
<li>Various domain files.
<li>Sample data sets, including "family", "king-rook-king", and "insert-after".
</ol>


Pointers to papers on FORTE can be found on our <!WA1><!WA1><!WA1><a
href="http://www.cs.utexas.edu/users/ml/ilp.html">ILP</a> and <!WA2><!WA2><!WA2><a href="http://www.cs.utexas.edu/users/ml/theory-rev.html">Theory
Revision</a> publication pages.  Below is the standard reference (click
on the open book image). <p> 

<! ===========================================================================>

<a name="forte-mlj-94.ps.Z" </a>

<b> <li> Refinement of First-Order Horn-Clause Domain Theories </b> <br>

Bradley L. Richards and Raymond J. Mooney <br>

<cite> Machine Learning</cite> 19,2 (1995), pp. 95-131. <p>

<blockquote> Knowledge acquisition is a difficult and time-consuming
task, and as error-prone as any human activity.  The task of
automatically improving an existing knowledge base using learning
methods is addressed by a new class of systems performing <i> theory
refinement</i>.  Until recently, such systems were limited to
propositional theories.  This paper presents a system, FORTE
(First-Order Revision of Theories from Examples), for refining
first-order Horn-clause theories.  Moving to a first-order
representation opens many new problem areas, such as logic program
debugging and qualitative modelling, that are beyond the reach of
propositional systems.  FORTE uses a hill-climbing approach to revise
theories.  It identifies possible errors in the theory and calls on a
library of operators to develop possible revisions.  The best revision
is implemented, and the process repeats until no further revisions are
possible.  Operators are drawn from a variety of sources, including
propositional theory refinement, first-order induction, and inverse
resolution.  FORTE has been tested in several domains including
logic programming and qualitative modelling.  </blockquote>

<!WA3><!WA3><!WA3><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/forte-mlj-94.ps.Z">
<!WA4><!WA4><!WA4><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p>


<hr>
<address><!WA5><!WA5><!WA5><a href="http://www.cs.utexas.edu/users/estlin/">estlin@cs.utexas.edu</a></address>
